Predictor variable

Make it a habit to examine the residuals of a fitted model, including deviations from a simple mean.
Check for normality by making a dot diagram or histogram. Plot the residuals against the predicted
values, against the predictor variables, and as a function of the time order in which themeasurements were made

Wittman (1941) developed a prognosis scale for predicting outcome of electroshock
therapy in schizophrenia, which consisted of 30 variables rated from social history and
psychiatric examination. The predictors ranged from semi-objective matters (such as
duration of psychosis) to highly interpretive judgments (such as anal-erotic vs. oral-erotic
character). None of the predictor variables was psychometric.

The employer-employee Relationship: A phenomenological Study of Retention and the Information Technology Worker In University of California data, I find evidence that observable background
characteristicsparticularly those describing the composition of the school, rather than the
individuals own backgroundare strong predictors of both SAT scores and collegiate
performance, and that much of the SATs apparent predictive power derives from its
association with these background characteristics.

THE RELATIONSHIP BETWEEN EMPLOYEE TURNOVER AND CUSTOMER SERVICE QUALITY IN CASINO RESTAURANTS The remaining panels present models for measures relating to school continuation
rates, defined as one minus the cumulative dropout rate. In Panel C, the dependent variable
is the fraction of students from the NELS 8th grade sample who were still in school at the
time of the 12th follow-up survey four years later.

Chronic lymphocytic leukemia (CLL) follows an extremely variable course with
survival ranging from months to decades. Recently, there has been major progress in
the identification of molecular and cellular markers that may predict the tendency for
disease progression in CLL patients. In particular, the mutational profile of Ig genes
and some cytogenetic abnormalities have been found to be important predictors of
prognosis in CLL. However, this progress has raised new questions about the biology,
prognosis and management of the disease, some of which are addressed here. ...

The interaction between these
two variables, however, provided a better predictor of beverage preference. Similar
results, in terms of the interaction between product choice and usage situation were
found by Green and Rao (1972), Belk (1974) and Srivastava et al. (1978). In a later
study, Srivastava (1980) examined the appropriateness of financial services in a
particular situation and found it to be relatively stable across situations, thus providing
further support for using consumption situations as a basis for segmenting the market.

In a classification problem, you typically have historical data (labeled examples)
and unlabeled examples. Each labeled example consists of multiple predictor
attributes and one target attribute (dependent variable). The value of the target
attribute is a class label. The unlabeled examples consist of the predictor attributes
only. The goal of classification is to construct a model using the historical data that
accurately predicts the label (class) of the unlabeled examples.

Statistical models were used to control for—i.e., remove—the impact of
socioeconomic factors that might account for the correlation between race/ethnicity and
credit scores. The inclusion of such controls slightly weakened, but by no means eliminated
(or accounted for) the association between minority status and credit scores. Among all
such control variables, race/ethnicity proved to be the most robust single predictor of credit
scores; in most instances it had a significantly greater impact than education, marital status,
income and housing values.

After studying this chapter you will be able to understand: How to classify and select multivariate techniques, that multiple regression predicts a metric dependent variable from a set of metric independent variables, that discriminant analysis classifies people or objects into categorical groups using several metric predictors.